Cloud Generalized Power Ordered Weighted Average Operator and Its Application to Linguistic Group Decision-Making

Jianwei Gao,Ru Yi

doi:10.3390/sym9080156

Abstract

In this paper, we develop a new linguistic aggregation operator based on the cloud model for solving linguistic group decision-making problem. First, an improved generating cloud method is proposed so as to transform linguistic variables into clouds, which modifies the limitation of the classical generating cloud method. We then address some new cloud algorithms, such as cloud possibility degree and cloud support degree which can be respectively used to compare clouds and determine the weights. Combining the cloud support degree with power aggregation operator, we develop a new cloud aggregation operator dubbed the cloud generalized power ordered weighted average (CGPOWA) operator. We study the properties of the CGPOWA operator and investigate its family including a wide range of aggregation operators such as the CGPA operator, CPOWA operator, CPOWGA operator, CPWQA operator, CWAA and CWGA operator. Furthermore, a new approach for linguistic group decision-making is presented on the basis of the improved generating cloud method and CGPOWA operator. Finally, an illustrative example is provided to examine the effectiveness and validity of the proposed approach.

Highlights

IntroductionIs the process of finding the best option from all of the feasible alternatives
As an important part of modern decision science, multiple criteria decision-making (MCDM)is the process of finding the best option from all of the feasible alternatives
The cloud generalized power ordered weighted average (CGPOWA) operator considers the relationships between the arguments provided by the decision maker (DM), while the cloud aggregating operators in Wang et al [41,42] do not; Our method presents a simple measure to compare different clouds by the cloud possibility degree (Equation (11)) and the ranking vector (Equation (13)), which requires no knowledge about the distribution of cloud drops, this is different from the score function [41] which needs to know the distribution of cloud drops

Summary

Introduction

Is the process of finding the best option from all of the feasible alternatives It consists of a single decision maker (DM), multiple decision criteria and multiple decision alternatives [1]. The increasing complexity of the socioeconomic environment makes it less possible for a single DM to consider all relevant aspects of a problem as many decision-making processes take place in group settings. This makes the multiple criteria group decision-making (MCGDM) become more and more attractive in management [2,3,4,5,6].

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